Marketplace Data/Decision Scientist

Required Skills Python SQL Machine Learning Data Science Dynamic Pricing Pricing Optimization marketplace optimization Job Description We're looking for a decision scientist to build the pricing and routing brain for a large lead-generation marketplace in the personal-injury legal vertical. This is real-time, money-on-the-line optimization — dynamic pricing, lead routing, and a self-improving loop that learns as it runs. If you've shipped pricing or bid optimization in production on a two-sided marketplace, this is squarely your kind of problem. This is not a generalist data science role. We need marketplace and pricing depth. What we're looking for Must-haves Hands-on experience with a two-sided marketplace, ad exchange, RTB system, or lead-gen platform Dynamic pricing or bid optimization shipped in production, where it moved real revenue Optimization under uncertainty: contextual bandits, reinforcement learning, or sequential decision-making Sound experimentation practice: A/B testing, causal inference, incrementality Strong Python and SQL, with the ability to own a model end to end 5–12 years of relevant experience Strong signals Background at a marketplace, adtech, martech, or insurtech company (think MediaAlpha, EverQuote, QuinStreet, The Trade Desk, Index Exchange, Root, Hippo, Lemonade, and similar) Native fluency in lead-gen mechanics: ping/post, waterfall routing, lead scoring, buyer bid response Nice to have Personal-injury legal or insurance lead-gen specifically Experience building a routing or matching system, not just a scoring model